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sdv-synthetic-data

Generate synthetic data with SDV for single-table and multi-table datasets.

Updated Jan 18, 2026
Search Tags:#machine learning#data analysis#data generation#synthetic data#privacy#sdv
One-click install
npx skills add https://github.com/mullzhang/skills --skill sdv-synthetic-data
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: sdv-synthetic-data
Source: https://github.com/mullzhang/skills/tree/main/skills/mullzhang/sdv-synthetic-data
Command: npx skills add https://github.com/mullzhang/skills --skill sdv-synthetic-data

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill automates the generation of synthetic data, enabling users to create realistic datasets for testing, development, or analysis without compromising privacy.

Core Features & Use Cases

  • Synthetic Data Generation: Create privacy-preserving synthetic data that mimics the patterns of real data.
  • Single-Table & Multi-Table Generation: Supports generating data for both individual tables and relational databases.
  • Time-Series Data: (Currently unsupported by existing scripts, but can be extended).
  • Quality Evaluation: Assess the quality of generated synthetic data against real data.
  • Use Case: A financial institution needs to test a new fraud detection model. They can use this Skill to generate a large, realistic synthetic dataset of transactions that mirrors their real data's statistical properties, without exposing sensitive customer information.

Quick Start

Use the sdv-synthetic-data skill to generate 1000 rows of synthetic data from the file 'customer_data.csv'.

Frequently Asked Questions about sdv-synthetic-data

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate synthetic data for testing without exposing sensitive customer information?▼

You can generate synthetic data using the Synthetic Data Vault (SDV) library to create realistic datasets that mimic real data patterns for testing and development without compromising privacy.

What synthesizers are available for synthetic data generation?▼

Synthetic data generation supports various synthesizers including GaussianCopula, CTGAN, and TVAE, with options for metadata configuration and constraint enforcement.

Can I generate synthetic data for relational databases with multiple tables?▼

Yes, synthetic data generation supports both single-table and multi-table generation, allowing you to create data for individual tables as well as relational databases.

How do I evaluate the quality of synthetic data against real data?▼

You can assess synthetic data quality using built-in evaluation features that compare the generated data against the statistical properties of your real data.

Does synthetic data generation work for time-series datasets?▼

Time-series data generation is currently unsupported by existing scripts, but the Synthetic Data Vault (SDV) library can be extended to support time-series datasets.

Do I need to configure metadata and constraints before generating synthetic data?▼

Yes, synthetic data generation provides options for metadata configuration and constraint enforcement to ensure the generated data accurately reflects your real data structure.

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